Subclass for random search tuning.
The random points are sampled by paradox::generate_design_random().
Source
Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281--305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.
Dictionary
This Tuner can be instantiated via the dictionary
mlr_tuners or with the associated sugar function tnr():
TunerRandomSearch$new() mlr_tuners$get("random_search") tnr("random_search")
Parallelization
In order to support general termination criteria and parallelization, we
evaluate points in a batch-fashion of size batch_size. Larger batches mean
we can parallelize more, smaller batches imply a more fine-grained checking
of termination criteria. A batch contains of batch_size times resampling$iters jobs.
E.g., if you set a batch size of 10 points and do a 5-fold cross validation, you can
utilize up to 50 cores.
Parallelization is supported via package future (see mlr3::benchmark()'s
section on parallelization for more details).
Logging
All Tuners use a logger (as implemented in lgr) from package
bbotk.
Use lgr::get_logger("bbotk") to access and control the logger.
Parameters
batch_sizeinteger(1)
Maximum number of points to try in a batch.
Progress Bars
$optimize() supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress() to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress").
See also
Package mlr3hyperband for hyperband tuning.
Other Tuner:
mlr_tuners_cmaes,
mlr_tuners_design_points,
mlr_tuners_gensa,
mlr_tuners_grid_search,
mlr_tuners_irace,
mlr_tuners_nloptr
Super classes
mlr3tuning::Tuner -> mlr3tuning::TunerFromOptimizer -> TunerRandomSearch
Methods
Public methods
Method new()
Creates a new instance of this R6 class.
Usage
TunerRandomSearch$new()
Method clone()
The objects of this class are cloneable with this method.
Usage
TunerRandomSearch$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
# retrieve task task = tsk("pima") # load learner and set search space learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)) # hyperparameter tuning on the pima indians diabetes data set instance = tune( method = "random_search", task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), term_evals = 10 ) # best performing hyperparameter configuration instance$result#> cp learner_param_vals x_domain classif.ce #> 1: -4.523676 <list[2]> <list[1]> 0.2265625#> cp classif.ce x_domain_cp runtime_learners timestamp #> 1: -6.410471 0.2773438 0.0016442491 0.012 2021-09-16 04:23:26 #> 2: -5.269410 0.2656250 0.0051466475 0.012 2021-09-16 04:23:26 #> 3: -7.059103 0.2773438 0.0008595488 0.012 2021-09-16 04:23:26 #> 4: -3.730501 0.2421875 0.0239808065 0.010 2021-09-16 04:23:26 #> 5: -6.727419 0.2773438 0.0011976204 0.012 2021-09-16 04:23:27 #> 6: -6.256072 0.2773438 0.0019187674 0.010 2021-09-16 04:23:27 #> 7: -5.754732 0.2656250 0.0031677556 0.012 2021-09-16 04:23:27 #> 8: -7.431878 0.2773438 0.0005920747 0.011 2021-09-16 04:23:27 #> 9: -4.523676 0.2265625 0.0108490725 0.011 2021-09-16 04:23:27 #> 10: -4.350720 0.2265625 0.0128975237 0.012 2021-09-16 04:23:27 #> batch_nr resample_result #> 1: 1 <ResampleResult[20]> #> 2: 2 <ResampleResult[20]> #> 3: 3 <ResampleResult[20]> #> 4: 4 <ResampleResult[20]> #> 5: 5 <ResampleResult[20]> #> 6: 6 <ResampleResult[20]> #> 7: 7 <ResampleResult[20]> #> 8: 8 <ResampleResult[20]> #> 9: 9 <ResampleResult[20]> #> 10: 10 <ResampleResult[20]># fit final model on complete data set learner$param_set$values = instance$result_learner_param_vals learner$train(task)